Overview

Dataset statistics

Number of variables13
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows5
Duplicate rows (%)0.5%
Total size in memory101.7 KiB
Average record size in memory104.1 B

Variable types

Categorical4
Numeric9

Alerts

Dataset has 5 (0.5%) duplicate rowsDuplicates
Date has a high cardinality: 136 distinct valuesHigh cardinality
region has a high cardinality: 54 distinct valuesHigh cardinality
AveragePrice is highly overall correlated with Total Volume and 8 other fieldsHigh correlation
Total Volume is highly overall correlated with AveragePrice and 7 other fieldsHigh correlation
4046 is highly overall correlated with AveragePrice and 7 other fieldsHigh correlation
4225 is highly overall correlated with AveragePrice and 7 other fieldsHigh correlation
4770 is highly overall correlated with AveragePrice and 6 other fieldsHigh correlation
Total Bags is highly overall correlated with AveragePrice and 7 other fieldsHigh correlation
Small Bags is highly overall correlated with AveragePrice and 7 other fieldsHigh correlation
Large Bags is highly overall correlated with AveragePrice and 5 other fieldsHigh correlation
XLarge Bags is highly overall correlated with AveragePrice and 6 other fieldsHigh correlation
type is highly overall correlated with AveragePriceHigh correlation
4770 has 237 (23.7%) zerosZeros
Large Bags has 249 (24.9%) zerosZeros
XLarge Bags has 704 (70.4%) zerosZeros

Reproduction

Analysis started2023-03-29 13:41:53.473322
Analysis finished2023-03-29 13:42:07.862540
Duration14.39 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

Date
Categorical

Distinct136
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2016-07-31
 
73
2015-11-01
 
62
2016-06-19
 
42
2015-12-20
 
37
2015-01-11
 
20
Other values (131)
766 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row2015-07-05
2nd row2015-06-07
3rd row2015-01-18
4th row2015-04-05
5th row2016-10-16

Common Values

ValueCountFrequency (%)
2016-07-31 73
 
7.3%
2015-11-01 62
 
6.2%
2016-06-19 42
 
4.2%
2015-12-20 37
 
3.7%
2015-01-11 20
 
2.0%
2015-04-05 13
 
1.3%
2015-01-04 12
 
1.2%
2015-03-22 10
 
1.0%
2015-07-19 10
 
1.0%
2016-02-07 10
 
1.0%
Other values (126) 711
71.1%

Length

2023-03-29T15:42:07.948311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016-07-31 73
 
7.3%
2015-11-01 62
 
6.2%
2016-06-19 42
 
4.2%
2015-12-20 37
 
3.7%
2015-01-11 20
 
2.0%
2015-04-05 13
 
1.3%
2015-01-04 12
 
1.2%
2015-03-22 10
 
1.0%
2015-07-19 10
 
1.0%
2016-02-07 10
 
1.0%
Other values (126) 711
71.1%

Most occurring characters

ValueCountFrequency (%)
0 2204
22.0%
1 2003
20.0%
- 2000
20.0%
2 1503
15.0%
5 628
 
6.3%
6 561
 
5.6%
7 385
 
3.9%
3 265
 
2.6%
4 166
 
1.7%
9 159
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8000
80.0%
Dash Punctuation 2000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2204
27.6%
1 2003
25.0%
2 1503
18.8%
5 628
 
7.8%
6 561
 
7.0%
7 385
 
4.8%
3 265
 
3.3%
4 166
 
2.1%
9 159
 
2.0%
8 126
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2204
22.0%
1 2003
20.0%
- 2000
20.0%
2 1503
15.0%
5 628
 
6.3%
6 561
 
5.6%
7 385
 
3.9%
3 265
 
2.6%
4 166
 
1.7%
9 159
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2204
22.0%
1 2003
20.0%
- 2000
20.0%
2 1503
15.0%
5 628
 
6.3%
6 561
 
5.6%
7 385
 
3.9%
3 265
 
2.6%
4 166
 
1.7%
9 159
 
1.6%

AveragePrice
Real number (ℝ)

Distinct173
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.41973
Minimum0.46
Maximum2.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-29T15:42:08.096913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile0.8495
Q11.07
median1.365
Q31.81
95-th percentile2.0205
Maximum2.75
Range2.29
Interquartile range (IQR)0.74

Descriptive statistics

Standard deviation0.41809428
Coefficient of variation (CV)0.29448859
Kurtosis-0.55525463
Mean1.41973
Median Absolute Deviation (MAD)0.345
Skewness0.370147
Sum1419.73
Variance0.17480283
MonotonicityNot monotonic
2023-03-29T15:42:08.274472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.02 65
 
6.5%
1.82 58
 
5.8%
0.99 52
 
5.2%
1.23 44
 
4.4%
1.85 16
 
1.6%
1.11 13
 
1.3%
1.87 13
 
1.3%
0.98 12
 
1.2%
1.38 12
 
1.2%
1.19 12
 
1.2%
Other values (163) 703
70.3%
ValueCountFrequency (%)
0.46 1
 
0.1%
0.51 1
 
0.1%
0.56 2
0.2%
0.57 1
 
0.1%
0.58 1
 
0.1%
0.6 2
0.2%
0.63 1
 
0.1%
0.65 3
0.3%
0.68 2
0.2%
0.72 2
0.2%
ValueCountFrequency (%)
2.75 1
0.1%
2.73 1
0.1%
2.72 2
0.2%
2.65 1
0.1%
2.63 1
0.1%
2.59 1
0.1%
2.51 1
0.1%
2.48 1
0.1%
2.46 1
0.1%
2.41 1
0.1%

Total Volume
Real number (ℝ)

Distinct801
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean749914.07
Minimum385.55
Maximum37130689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-29T15:42:08.451964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum385.55
5-th percentile1958.1395
Q18647.5025
median87961.135
Q3453747.33
95-th percentile3666657.1
Maximum37130689
Range37130303
Interquartile range (IQR)445099.83

Descriptive statistics

Standard deviation2791606.2
Coefficient of variation (CV)3.7225681
Kurtosis108.13685
Mean749914.07
Median Absolute Deviation (MAD)85852.095
Skewness9.5940705
Sum7.4991407 × 108
Variance7.793065 × 1012
MonotonicityNot monotonic
2023-03-29T15:42:08.614561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14788.87 64
 
6.4%
2109.04 55
 
5.5%
278178.65 41
 
4.1%
1139347.98 32
 
3.2%
1614.56 12
 
1.2%
115579.5 1
 
0.1%
1275952.93 1
 
0.1%
9140.79 1
 
0.1%
261084.13 1
 
0.1%
7867.61 1
 
0.1%
Other values (791) 791
79.1%
ValueCountFrequency (%)
385.55 1
0.1%
515.01 1
0.1%
593.39 1
0.1%
761.86 1
0.1%
792.97 1
0.1%
858.83 1
0.1%
872.07 1
0.1%
964.25 1
0.1%
979.74 1
0.1%
1014.02 1
0.1%
ValueCountFrequency (%)
37130688.91 1
0.1%
35734613.9 1
0.1%
33254911.87 1
0.1%
32994014.16 1
0.1%
30652211.08 1
0.1%
26240072.11 1
0.1%
9032180.67 1
0.1%
7707711.9 1
0.1%
7661483.37 1
0.1%
7281887.35 1
0.1%

4046
Real number (ℝ)

Distinct795
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean261937.71
Minimum0
Maximum14699605
Zeros7
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-29T15:42:08.812001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.5355
Q1895.68
median6212.92
Q376788.78
95-th percentile1376986.9
Maximum14699605
Range14699605
Interquartile range (IQR)75893.1

Descriptive statistics

Standard deviation1133462.4
Coefficient of variation (CV)4.3272213
Kurtosis99.984644
Mean261937.71
Median Absolute Deviation (MAD)6195.635
Skewness9.1416408
Sum2.6193771 × 108
Variance1.2847371 × 1012
MonotonicityNot monotonic
2023-03-29T15:42:09.020443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
895.68 64
 
6.4%
1331.43 55
 
5.5%
67619.73 41
 
4.1%
13998.35 32
 
3.2%
1091.28 12
 
1.2%
0 7
 
0.7%
18600.18 1
 
0.1%
2.48 1
 
0.1%
131414.8 1
 
0.1%
4161.89 1
 
0.1%
Other values (785) 785
78.5%
ValueCountFrequency (%)
0 7
0.7%
1.24 1
 
0.1%
2.48 1
 
0.1%
2.59 1
 
0.1%
3.64 1
 
0.1%
4.77 1
 
0.1%
4.82 1
 
0.1%
4.98 1
 
0.1%
5.04 1
 
0.1%
5.05 1
 
0.1%
ValueCountFrequency (%)
14699604.93 1
0.1%
14643465.52 1
0.1%
13282222.98 1
0.1%
13003371.07 1
0.1%
12196225.93 1
0.1%
8683958.8 1
0.1%
4794142.14 1
0.1%
4075881.34 1
0.1%
3851189.34 1
0.1%
3666928.36 1
0.1%

4225
Real number (ℝ)

Distinct801
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284227.4
Minimum0
Maximum13926693
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-29T15:42:09.188991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.71
Q12726.44
median26918.375
Q3146266.21
95-th percentile1177740.5
Maximum13926693
Range13926693
Interquartile range (IQR)143539.77

Descriptive statistics

Standard deviation1065258.7
Coefficient of variation (CV)3.7479098
Kurtosis109.73567
Mean284227.4
Median Absolute Deviation (MAD)26868.425
Skewness9.7224985
Sum2.842274 × 108
Variance1.134776 × 1012
MonotonicityNot monotonic
2023-03-29T15:42:09.370508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10096.25 64
 
6.4%
21.71 55
 
5.5%
122627.22 41
 
4.1%
867406.68 32
 
3.2%
49.95 12
 
1.2%
67122.02 1
 
0.1%
714407.25 1
 
0.1%
154.07 1
 
0.1%
84541.6 1
 
0.1%
2885.72 1
 
0.1%
Other values (791) 791
79.1%
ValueCountFrequency (%)
0 1
0.1%
1.26 1
0.1%
3.45 1
0.1%
5.8 1
0.1%
5.92 1
0.1%
6.26 1
0.1%
6.42 1
0.1%
7.16 1
0.1%
7.19 1
0.1%
8.76 1
0.1%
ValueCountFrequency (%)
13926692.62 1
0.1%
13733124.48 1
0.1%
13244466.6 1
0.1%
12009228.05 1
0.1%
11410478.34 1
0.1%
11083513.46 1
0.1%
3517891.27 1
0.1%
3460743.8 1
0.1%
3361015.9 1
0.1%
2896879.68 1
0.1%

4770
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct576
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20267.923
Minimum0
Maximum1326422.6
Zeros237
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-29T15:42:09.546069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.445
median495.8
Q38544.8875
95-th percentile97885.241
Maximum1326422.6
Range1326422.6
Interquartile range (IQR)8540.4425

Descriptive statistics

Standard deviation93649.678
Coefficient of variation (CV)4.6205857
Kurtosis106.80288
Mean20267.923
Median Absolute Deviation (MAD)495.8
Skewness9.5605068
Sum20267923
Variance8.7702621 × 109
MonotonicityNot monotonic
2023-03-29T15:42:09.733538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 237
23.7%
495.8 64
 
6.4%
12.57 55
 
5.5%
15409.3 41
 
4.1%
803.83 32
 
3.2%
367.91 1
 
0.1%
14380.96 1
 
0.1%
905.77 1
 
0.1%
12917.42 1
 
0.1%
36.04 1
 
0.1%
Other values (566) 566
56.6%
ValueCountFrequency (%)
0 237
23.7%
1.44 1
 
0.1%
1.58 1
 
0.1%
2.63 1
 
0.1%
2.73 1
 
0.1%
2.74 1
 
0.1%
2.91 1
 
0.1%
3.08 1
 
0.1%
3.22 1
 
0.1%
3.32 1
 
0.1%
ValueCountFrequency (%)
1326422.56 1
0.1%
1188118.95 1
0.1%
1070576.07 1
0.1%
1017157.74 1
0.1%
1014520.88 1
0.1%
697833.12 1
0.1%
556972.27 1
0.1%
540680.89 1
0.1%
356022.1 1
0.1%
352247.15 1
0.1%

Total Bags
Real number (ℝ)

Distinct801
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183477.1
Minimum0
Maximum10863029
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-29T15:42:09.920037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile521.3105
Q13301.14
median33113.605
Q3106308.4
95-th percentile930244.17
Maximum10863029
Range10863029
Interquartile range (IQR)103007.26

Descriptive statistics

Standard deviation604482.27
Coefficient of variation (CV)3.2945924
Kurtosis127.38009
Mean183477.1
Median Absolute Deviation (MAD)32370.275
Skewness9.5254951
Sum1.834771 × 108
Variance3.6539881 × 1011
MonotonicityNot monotonic
2023-03-29T15:42:10.105579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3301.14 64
 
6.4%
743.33 55
 
5.5%
72522.4 41
 
4.1%
257139.12 32
 
3.2%
473.33 12
 
1.2%
41505.82 1
 
0.1%
529895.86 1
 
0.1%
8984.24 1
 
0.1%
40566.09 1
 
0.1%
820 1
 
0.1%
Other values (791) 791
79.1%
ValueCountFrequency (%)
0 1
0.1%
12.94 1
0.1%
25.15 1
0.1%
40 1
0.1%
40.19 1
0.1%
45.84 1
0.1%
59.12 1
0.1%
80.97 1
0.1%
109.47 1
0.1%
136.18 1
0.1%
ValueCountFrequency (%)
10863029.04 1
0.1%
6464119.81 1
0.1%
5429599.36 1
0.1%
5310327.3 1
0.1%
4957516.02 1
0.1%
4908090.63 1
0.1%
2868822.6 1
0.1%
2707752.65 1
0.1%
2285895.6 1
0.1%
2246865.14 1
0.1%

Small Bags
Real number (ℝ)

Distinct790
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143267.44
Minimum0
Maximum8555283.7
Zeros5
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-29T15:42:10.284065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile263.349
Q11666.1775
median20016.585
Q381615.28
95-th percentile695997.32
Maximum8555283.7
Range8555283.7
Interquartile range (IQR)79949.102

Descriptive statistics

Standard deviation487250.35
Coefficient of variation (CV)3.4009846
Kurtosis124.12477
Mean143267.44
Median Absolute Deviation (MAD)19546.585
Skewness9.5296762
Sum1.4326744 × 108
Variance2.374129 × 1011
MonotonicityNot monotonic
2023-03-29T15:42:10.466578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3301.14 64
 
6.4%
743.33 55
 
5.5%
67694 41
 
4.1%
212615.2 32
 
3.2%
473.33 12
 
1.2%
0 5
 
0.5%
286.67 2
 
0.2%
916.67 2
 
0.2%
143.33 2
 
0.2%
330 2
 
0.2%
Other values (780) 783
78.3%
ValueCountFrequency (%)
0 5
0.5%
3.33 1
 
0.1%
7.43 1
 
0.1%
12.94 1
 
0.1%
25.15 1
 
0.1%
26.67 1
 
0.1%
30 2
 
0.2%
40 1
 
0.1%
40.19 1
 
0.1%
40.46 1
 
0.1%
ValueCountFrequency (%)
8555283.68 1
0.1%
5517909.51 1
0.1%
4627251.87 1
0.1%
4391461.66 1
0.1%
4129138.63 1
0.1%
3918658.43 1
0.1%
2122673.09 1
0.1%
1911608.83 1
0.1%
1722082.22 1
0.1%
1665446.71 1
0.1%

Large Bags
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct670
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38216.978
Minimum0
Maximum2214419.3
Zeros249
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-29T15:42:10.650088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.445
median1066.285
Q317323.568
95-th percentile185715.35
Maximum2214419.3
Range2214419.3
Interquartile range (IQR)17321.123

Descriptive statistics

Standard deviation134768.51
Coefficient of variation (CV)3.5264041
Kurtosis95.462004
Mean38216.978
Median Absolute Deviation (MAD)1066.285
Skewness8.2837023
Sum38216978
Variance1.816255 × 1010
MonotonicityNot monotonic
2023-03-29T15:42:10.827644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 249
 
24.9%
83.4 41
 
4.1%
44523.92 32
 
3.2%
3.33 7
 
0.7%
23.33 3
 
0.3%
6.67 3
 
0.3%
43.33 2
 
0.2%
10017.28 1
 
0.1%
11396.65 1
 
0.1%
31781.23 1
 
0.1%
Other values (660) 660
66.0%
ValueCountFrequency (%)
0 249
24.9%
1.83 1
 
0.1%
2.65 1
 
0.1%
2.75 1
 
0.1%
3.08 1
 
0.1%
3.33 7
 
0.7%
3.79 1
 
0.1%
3.97 1
 
0.1%
4.44 1
 
0.1%
4.71 1
 
0.1%
ValueCountFrequency (%)
2214419.29 1
0.1%
1352291.24 1
0.1%
1320163.95 1
0.1%
1003906.67 1
0.1%
999727.7 1
0.1%
898027.46 1
0.1%
888237.91 1
0.1%
779807.55 1
0.1%
725218.35 1
0.1%
696042.61 1
0.1%

XLarge Bags
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct255
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1992.6885
Minimum0
Maximum93326.07
Zeros704
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-03-29T15:42:10.992172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q350.86
95-th percentile10313.553
Maximum93326.07
Range93326.07
Interquartile range (IQR)50.86

Descriptive statistics

Standard deviation7250.8038
Coefficient of variation (CV)3.6387041
Kurtosis47.868771
Mean1992.6885
Median Absolute Deviation (MAD)0
Skewness6.1507956
Sum1992688.5
Variance52574155
MonotonicityNot monotonic
2023-03-29T15:42:11.316306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 704
70.4%
4745 42
 
4.2%
2.85 2
 
0.2%
134.72 1
 
0.1%
3161.37 1
 
0.1%
4025 1
 
0.1%
1363.39 1
 
0.1%
26022.01 1
 
0.1%
40842.24 1
 
0.1%
3875.64 1
 
0.1%
Other values (245) 245
 
24.5%
ValueCountFrequency (%)
0 704
70.4%
1.87 1
 
0.1%
2.25 1
 
0.1%
2.7 1
 
0.1%
2.85 2
 
0.2%
3.06 1
 
0.1%
3.09 1
 
0.1%
3.22 1
 
0.1%
3.24 1
 
0.1%
3.39 1
 
0.1%
ValueCountFrequency (%)
93326.07 1
0.1%
64348.68 1
0.1%
54249.87 1
0.1%
53733.65 1
0.1%
48182.84 1
0.1%
47983.17 1
0.1%
46909.66 1
0.1%
46466.7 1
0.1%
45140.96 1
0.1%
44790.27 1
0.1%

type
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
organic
513 
conventional
487 

Length

Max length12
Median length7
Mean length9.435
Min length7

Characters and Unicode

Total characters9435
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconventional
2nd roworganic
3rd roworganic
4th rowconventional
5th roworganic

Common Values

ValueCountFrequency (%)
organic 513
51.3%
conventional 487
48.7%

Length

2023-03-29T15:42:11.466626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T15:42:11.596279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
organic 513
51.3%
conventional 487
48.7%

Most occurring characters

ValueCountFrequency (%)
n 1974
20.9%
o 1487
15.8%
a 1000
10.6%
i 1000
10.6%
c 1000
10.6%
r 513
 
5.4%
g 513
 
5.4%
v 487
 
5.2%
e 487
 
5.2%
t 487
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9435
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1974
20.9%
o 1487
15.8%
a 1000
10.6%
i 1000
10.6%
c 1000
10.6%
r 513
 
5.4%
g 513
 
5.4%
v 487
 
5.2%
e 487
 
5.2%
t 487
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 9435
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1974
20.9%
o 1487
15.8%
a 1000
10.6%
i 1000
10.6%
c 1000
10.6%
r 513
 
5.4%
g 513
 
5.4%
v 487
 
5.2%
e 487
 
5.2%
t 487
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9435
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1974
20.9%
o 1487
15.8%
a 1000
10.6%
i 1000
10.6%
c 1000
10.6%
r 513
 
5.4%
g 513
 
5.4%
v 487
 
5.2%
e 487
 
5.2%
t 487
 
5.2%

year
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2015
437 
2016
390 
2017
173 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2016

Common Values

ValueCountFrequency (%)
2015 437
43.7%
2016 390
39.0%
2017 173
 
17.3%

Length

2023-03-29T15:42:11.714992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T15:42:11.844613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2015 437
43.7%
2016 390
39.0%
2017 173
 
17.3%

Most occurring characters

ValueCountFrequency (%)
2 1000
25.0%
0 1000
25.0%
1 1000
25.0%
5 437
10.9%
6 390
 
9.8%
7 173
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1000
25.0%
0 1000
25.0%
1 1000
25.0%
5 437
10.9%
6 390
 
9.8%
7 173
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 4000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1000
25.0%
0 1000
25.0%
1 1000
25.0%
5 437
10.9%
6 390
 
9.8%
7 173
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1000
25.0%
0 1000
25.0%
1 1000
25.0%
5 437
10.9%
6 390
 
9.8%
7 173
 
4.3%

region
Categorical

Distinct54
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
BaltimoreWashington
76 
RichmondNorfolk
 
64
Jacksonville
 
62
NewYork
 
51
MiamiFtLauderdale
 
28
Other values (49)
719 

Length

Max length19
Median length15
Mean length11.039
Min length4

Characters and Unicode

Total characters11039
Distinct characters45
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPittsburgh
2nd rowBuffaloRochester
3rd rowMiamiFtLauderdale
4th rowSanFrancisco
5th rowBaltimoreWashington

Common Values

ValueCountFrequency (%)
BaltimoreWashington 76
 
7.6%
RichmondNorfolk 64
 
6.4%
Jacksonville 62
 
6.2%
NewYork 51
 
5.1%
MiamiFtLauderdale 28
 
2.8%
Pittsburgh 24
 
2.4%
West 24
 
2.4%
Boise 21
 
2.1%
SouthCentral 20
 
2.0%
NewOrleansMobile 20
 
2.0%
Other values (44) 610
61.0%

Length

2023-03-29T15:42:11.978296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
baltimorewashington 76
 
7.6%
richmondnorfolk 64
 
6.4%
jacksonville 62
 
6.2%
newyork 51
 
5.1%
miamiftlauderdale 28
 
2.8%
pittsburgh 24
 
2.4%
west 24
 
2.4%
boise 21
 
2.1%
southcentral 20
 
2.0%
neworleansmobile 20
 
2.0%
Other values (44) 610
61.0%

Most occurring characters

ValueCountFrequency (%)
o 1058
 
9.6%
a 1015
 
9.2%
e 832
 
7.5%
n 785
 
7.1%
i 759
 
6.9%
t 732
 
6.6%
l 695
 
6.3%
r 659
 
6.0%
s 555
 
5.0%
h 375
 
3.4%
Other values (35) 3574
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9423
85.4%
Uppercase Letter 1616
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1058
11.2%
a 1015
10.8%
e 832
8.8%
n 785
8.3%
i 759
 
8.1%
t 732
 
7.8%
l 695
 
7.4%
r 659
 
7.0%
s 555
 
5.9%
h 375
 
4.0%
Other values (13) 1958
20.8%
Uppercase Letter
ValueCountFrequency (%)
N 194
12.0%
S 194
12.0%
B 130
 
8.0%
R 125
 
7.7%
W 120
 
7.4%
C 103
 
6.4%
L 96
 
5.9%
P 86
 
5.3%
M 68
 
4.2%
D 65
 
4.0%
Other values (12) 435
26.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 11039
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1058
 
9.6%
a 1015
 
9.2%
e 832
 
7.5%
n 785
 
7.1%
i 759
 
6.9%
t 732
 
6.6%
l 695
 
6.3%
r 659
 
6.0%
s 555
 
5.0%
h 375
 
3.4%
Other values (35) 3574
32.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11039
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1058
 
9.6%
a 1015
 
9.2%
e 832
 
7.5%
n 785
 
7.1%
i 759
 
6.9%
t 732
 
6.6%
l 695
 
6.3%
r 659
 
6.0%
s 555
 
5.0%
h 375
 
3.4%
Other values (35) 3574
32.4%

Interactions

2023-03-29T15:42:06.057397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:54.931111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:56.378241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:57.779497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:59.124900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:00.517177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:01.905496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:03.242890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:04.568346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:06.190010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:55.079713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:56.514913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:57.927099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:59.261534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:00.664813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:02.047087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:03.385508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:04.700992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:06.357587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:55.237292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:56.673453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:58.082685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:59.416120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:00.836322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:02.207658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:03.535109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:04.861572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:06.515141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:55.386923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:56.836017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:58.236275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:59.576726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:00.999886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:02.368228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:03.699668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:05.022158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:06.668731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:55.543473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:56.996589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:58.383880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:59.746238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:01.168436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:02.513839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:03.857277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:05.190685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:06.814342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:55.706038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:57.151175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:58.533481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:59.904813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:01.317039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:02.669422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:04.009867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:05.352251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:06.966933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:55.863617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:57.309776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:58.686070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:00.059401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:01.465641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:02.809080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:04.156447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:05.659429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:07.106560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:55.995265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:57.451404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:58.836670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:00.218974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:01.609282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:02.943688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:04.301061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:05.793072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:07.243195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:56.246592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:57.621915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:41:58.982311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:00.370569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:01.753870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:03.099274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:04.431712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-29T15:42:05.933696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-29T15:42:12.109905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
AveragePriceTotal Volume404642254770Total BagsSmall BagsLarge BagsXLarge Bagstypeyearregion
AveragePrice1.000-0.695-0.682-0.600-0.529-0.707-0.639-0.584-0.5180.7340.2430.399
Total Volume-0.6951.0000.8620.9570.7900.9600.9310.7280.6060.2250.0000.377
4046-0.6820.8621.0000.7650.7120.8150.8070.6160.6130.2220.0000.380
4225-0.6000.9570.7651.0000.7940.8910.8690.6720.5880.2000.0490.432
4770-0.5290.7900.7120.7941.0000.7350.7700.4400.6520.1670.0680.273
Total Bags-0.7070.9600.8150.8910.7351.0000.9560.7770.6040.1910.1150.358
Small Bags-0.6390.9310.8070.8690.7700.9561.0000.6310.6200.1890.1090.315
Large Bags-0.5840.7280.6160.6720.4400.7770.6311.0000.3900.1670.0720.237
XLarge Bags-0.5180.6060.6130.5880.6520.6040.6200.3901.0000.2360.0990.211
type0.7340.2250.2220.2000.1670.1910.1890.1670.2361.0000.0000.409
year0.2430.0000.0000.0490.0680.1150.1090.0720.0990.0001.0000.334
region0.3990.3770.3800.4320.2730.3580.3150.2370.2110.4090.3341.000

Missing values

2023-03-29T15:42:07.449644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-29T15:42:07.744854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateAveragePriceTotal Volume404642254770Total BagsSmall BagsLarge BagsXLarge Bagstypeyearregion
02015-07-051.23115579.501635.6067122.025316.0641505.8231488.5410017.280.00conventional2015Pittsburgh
12015-06-071.804777.0445.84167.380.004563.824563.820.000.00organic2015BuffaloRochester
22015-01-181.871459.371054.7817.920.00386.67386.670.000.00organic2015MiamiFtLauderdale
32015-04-051.33742539.71143671.36473142.5535650.7090075.1088603.821025.45445.83conventional2015SanFrancisco
42016-10-161.9516843.53824.959095.02680.306243.266226.1417.120.00organic2016BaltimoreWashington
52015-01-040.94461607.33244152.26165299.3315302.7536852.9930884.295595.00373.70conventional2015SanDiego
62015-07-051.9829929.578841.3420807.76280.470.000.000.000.00organic2015Seattle
72016-08-141.489978.31899.714522.420.004556.182277.222278.960.00organic2016Nashville
82015-08-231.7211555.754288.753541.08635.313090.613087.862.750.00organic2015BaltimoreWashington
92016-10-091.2989506.2924596.3827239.36953.0536717.5032322.384395.120.00conventional2016Pittsburgh
DateAveragePriceTotal Volume404642254770Total BagsSmall BagsLarge BagsXLarge Bagstypeyearregion
9902017-07-091.52101331.412899.4681929.02494.5116008.428213.255210.172585.00conventional2017Albany
9912016-06-190.99278178.6567619.73122627.2215409.3072522.4067694.0083.404745.00conventional2016RichmondNorfolk
9922016-03-271.403215.76101.001477.500.001637.26663.33973.930.00organic2016Indianapolis
9932015-01-041.811339.36754.00124.330.00461.03447.0114.020.00organic2015Pittsburgh
9942016-03-200.743374876.94928106.16901464.7291298.871454007.191301235.10134711.2118060.88conventional2016LosAngeles
9952015-11-011.822109.041331.4321.7112.57743.33743.330.000.00organic2015Jacksonville
9962016-07-312.0214788.87895.6810096.25495.803301.143301.140.000.00organic2016BaltimoreWashington
9972015-11-081.404144.82443.102827.560.00874.16413.97460.190.00organic2015Columbus
9982015-12-130.95407482.832970.12369076.25116.8435319.6235319.620.000.00conventional2015NorthernNewEngland
9992015-11-011.822109.041331.4321.7112.57743.33743.330.000.00organic2015Jacksonville

Duplicate rows

Most frequently occurring

DateAveragePriceTotal Volume404642254770Total BagsSmall BagsLarge BagsXLarge Bagstypeyearregion# duplicates
42016-07-312.0214788.87895.6810096.25495.803301.143301.140.000.0organic2016BaltimoreWashington64
12015-11-011.822109.041331.4321.7112.57743.33743.330.000.0organic2015Jacksonville55
32016-06-190.99278178.6567619.73122627.2215409.3072522.4067694.0083.404745.0conventional2016RichmondNorfolk41
22015-12-201.231139347.9813998.35867406.68803.83257139.12212615.2044523.920.0conventional2015NewYork32
02015-01-111.851614.561091.2849.950.00473.33473.330.000.0organic2015MiamiFtLauderdale12